Multi-sensor Multi-cue Fusion for Object Detection in Video Surveillance
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Visual system based on artificial retina for motion detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Survey on contemporary remote surveillance systems for public safety
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Modeling and assessing quality of information in multisensor multimedia monitoring systems
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Fusing multiple video sensors for surveillance
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Sensor management: a new paradigm for automatic video surveillance
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
FQAS'11 Proceedings of the 9th international conference on Flexible Query Answering Systems
A RELIEF-based modality weighting approach for multimodal information retrieval
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
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In this correspondence, we address the problem of fusing data for object tracking for video surveillance. The fusion process is dynamically regulated to take into account the performance of the sensors in detecting and tracking the targets. This is performed through a function that adjusts the measurement error covariance associated with the position information of each target according to the quality of its segmentation. In this manner, localization errors due to incorrect segmentation of the blobs are reduced thus improving tracking accuracy. Experimental results on video sequences of outdoor environments show the effectiveness of the proposed approach.